The approach of conformal prediction ([1]}) is related to probabilistic regression in that both attempt to go beyond a mere point estimate on regression problems. Conformal prediction uses any machine learning model in conjunction with a user-specified confidence level to predict a range of values for the target, such that the target falls into the interval with the appropriate confidence. However, it differs from probabilistic regression in that it does not explicitly output a probability density function. Similarly, approaches like Quantile Regression ([2]}), in conjunction with Bayesian Methods ([3]}, [4]}), Random Forests ([5]}), or Neural Networks ([6]},[7]}) have been attempted, but do not output a full conditional probability density function and/or are not straightforward to use.
